Faceting

import numpy as np

from lets_plot import *
import pandas as pd
import vega_datasets
import seaborn as sns
LetsPlot.setup_html()

data = sns.load_dataset("penguins")
data

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female
...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

344 rows × 7 columns

ggplot() + geom_point(data=data, mapping=aes(x='bill_depth_mm', y='flipper_length_mm', color='species'))

ggplot() + geom_point(
    data=data, 
    mapping=aes(
        x='bill_depth_mm', 
        y='flipper_length_mm',
    )
) + facet_grid(x='species')

ggplot() + geom_point(
    data=data, 
    mapping=aes(
        x='bill_depth_mm', 
        y='flipper_length_mm',
        color='species'
    )
) + facet_grid(x='species')

ggplot()+ geom_point(
    data=data[['bill_depth_mm', 'flipper_length_mm']], 
    mapping=aes(
        x='bill_depth_mm', 
        y='flipper_length_mm',
    ), color='lightgrey'
) + geom_point(
    data=data, 
    mapping=aes(
        x='bill_depth_mm', 
        y='flipper_length_mm',
        color='species'
    )
) + facet_grid(x='species')

ggplot()+ geom_point(
    data=data[['bill_depth_mm', 'flipper_length_mm']], 
    mapping=aes(
        x='bill_depth_mm', 
        y='flipper_length_mm',
    )
) + geom_point(
    data=data, 
    mapping=aes(
        x='bill_depth_mm', 
        y='flipper_length_mm',
        color='species'
    )
) + facet_grid(x='species', y='sex')

Visualizing many variables

ggplot() + geom_point(data=data, mapping=aes(x='bill_depth_mm', y='flipper_length_mm', size='bill_length_mm', color='body_mass_g'))

ggplot() + geom_point(
    data=data, 
    mapping=aes(
        x='bill_depth_mm', 
        y='flipper_length_mm',
        color='bill_length_mm',
        size='body_mass_g',
    )
) + facet_grid(x='species', y='sex')

melted = data.reset_index().melt(id_vars=["index", "species", "island", "sex"])
merged = melted.merge(melted,  on=["index", "species", "island", "sex"]) 

ggplot() + geom_point(
    data=merged, 
    mapping=aes(
        x='value_x', 
        y='value_y',
        color='species'
    )
) + facet_grid(x='variable_x', y='variable_y',  scales='free')

import altair as alt

for field in ['bill_depth_mm', 'flipper_length_mm', 'bill_length_mm', 'body_mass_g']:
    data[field] = (data[field] - data[field].mean()) / data[field].std()

alt.Chart(data, width=500).transform_window(
    index='count()'
).transform_fold(
    ['bill_depth_mm', 'flipper_length_mm', 'bill_length_mm', 'body_mass_g']
).mark_line().encode(
    x='key:N',
    y='value:Q',
    color='species:N',
    detail='index:N',
    opacity=alt.value(0.5)
)

import altair as alt
from vega_datasets import data

source = data.iris()

alt.Chart(source, width=500).transform_window(
    index='count()'
).transform_fold(
    ['petalLength', 'petalWidth', 'sepalLength', 'sepalWidth']
).mark_line().encode(
    x='key:N',
    y='value:Q',
    color='species:N',
    detail='index:N',
    opacity=alt.value(0.5)
)

Tiles and raster marks

from vega_datasets import data

data = data.windvectors()

data

	longitude	latitude	dir	dirCat	speed
0	0.125	45.125	228	225	3.12
1	0.375	45.125	228	225	3.24
2	0.625	45.125	229	225	3.34
3	0.875	45.125	229	225	3.44
4	1.125	45.125	228	225	3.48
...	...	...	...	...	...
4795	-1.125	59.875	155	150	5.96
4796	-0.875	59.875	154	150	6.34
4797	-0.625	59.875	153	150	6.71
4798	-0.375	59.875	152	150	7.09
4799	-0.125	59.875	152	150	7.48

4800 rows × 5 columns

ggplot() + geom_point(data=data, mapping=aes(x='longitude', y='latitude', color='speed')) + ggsize(1000, 600)

ggplot() + geom_point(data=data, mapping=aes(x='longitude', y='latitude', color='speed')) + scale_color_brewer('div', palette='RdBu', direction=-1) + ggsize(1000, 600)

ggplot() + geom_tile(data=data, mapping=aes(x='longitude', y='latitude', fill='speed')) + scale_fill_brewer('div', palette='RdBu', direction=-1) + ggsize(1000, 600)

ggplot() + geom_raster(data=data, mapping=aes(x='longitude', y='latitude', fill='speed')) + scale_fill_brewer('div', palette='RdBu', direction=-1) + ggsize(1000, 600)

2-D Histograms

from vega_datasets import data

data = data.movies()

ggplot() + geom_point(data=data, mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating', color='Major_Genre', label='Name')) + ggsize(1000, 600)

ggplot() + geom_point(
    data=data[['IMDB_Rating', 'Rotten_Tomatoes_Rating']], 
    mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating'), color='lightgrey'
) + geom_point(
    data=data, 
    mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating', color='Major_Genre', label='Name')
)  + facet_wrap('Major_Genre') +  ggsize(1600, 1000)

ggplot() + geom_bin2d(data=data, mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating'), bins=[20, 20])

ggplot() + geom_bin2d(data=data, mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating'), bins=[20, 20]) \
    + coord_cartesian()

ggplot() + geom_bin2d(data=data, mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating'), bins=[20, 20]) \
    + coord_cartesian() + scale_fill_brewer('div', palette='Spectral', direction=-1) 

data

	Title	US_Gross	Worldwide_Gross	US_DVD_Sales	Production_Budget	Release_Date	MPAA_Rating	Running_Time_min	Distributor	Source	Major_Genre	Creative_Type	Director	Rotten_Tomatoes_Rating	IMDB_Rating	IMDB_Votes
0	The Land Girls	146083.0	146083.0	NaN	8000000.0	Jun 12 1998	R	NaN	Gramercy	None	None	None	None	NaN	6.1	1071.0
1	First Love, Last Rites	10876.0	10876.0	NaN	300000.0	Aug 07 1998	R	NaN	Strand	None	Drama	None	None	NaN	6.9	207.0
2	I Married a Strange Person	203134.0	203134.0	NaN	250000.0	Aug 28 1998	None	NaN	Lionsgate	None	Comedy	None	None	NaN	6.8	865.0
3	Let's Talk About Sex	373615.0	373615.0	NaN	300000.0	Sep 11 1998	None	NaN	Fine Line	None	Comedy	None	None	13.0	NaN	NaN
4	Slam	1009819.0	1087521.0	NaN	1000000.0	Oct 09 1998	R	NaN	Trimark	Original Screenplay	Drama	Contemporary Fiction	None	62.0	3.4	165.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3196	Zack and Miri Make a Porno	31452765.0	36851125.0	21240321.0	24000000.0	Oct 31 2008	R	101.0	Weinstein Co.	Original Screenplay	Comedy	Contemporary Fiction	Kevin Smith	65.0	7.0	55687.0
3197	Zodiac	33080084.0	83080084.0	20983030.0	85000000.0	Mar 02 2007	R	157.0	Paramount Pictures	Based on Book/Short Story	Thriller/Suspense	Dramatization	David Fincher	89.0	NaN	NaN
3198	Zoom	11989328.0	12506188.0	6679409.0	35000000.0	Aug 11 2006	PG	NaN	Sony Pictures	Based on Comic/Graphic Novel	Adventure	Super Hero	Peter Hewitt	3.0	3.4	7424.0
3199	The Legend of Zorro	45575336.0	141475336.0	NaN	80000000.0	Oct 28 2005	PG	129.0	Sony Pictures	Remake	Adventure	Historical Fiction	Martin Campbell	26.0	5.7	21161.0
3200	The Mask of Zorro	93828745.0	233700000.0	NaN	65000000.0	Jul 17 1998	PG-13	136.0	Sony Pictures	Remake	Adventure	Historical Fiction	Martin Campbell	82.0	6.7	4789.0

3201 rows × 16 columns

def density(data, x, y, bw=[0.25, 2.5]):
    x_dist = ((x - data['IMDB_Rating']) / bw[0]) ** 2
    y_dist = ((y - data['Rotten_Tomatoes_Rating']) / bw[1]) ** 2
    return np.exp(-(x_dist + y_dist)).mean()

x_coords, y_coords = np.meshgrid(np.linspace(0, 10, 200), np.linspace(0, 100, 200))
xy = np.stack([x_coords.flatten(), y_coords.flatten()]).T
data = pd.DataFrame(dict(
    x=xy[:, 0],
    y=xy[:, 1],
    density=[density(data, xi, yi) for (xi, yi) in xy]
))

ggplot() + geom_raster(data=data, mapping=aes(x='x', y='y', fill='density')) + scale_fill_brewer('div', palette='RdBu', direction=-1) + ggsize(1000, 600) + coord_cartesian()

ggplot() + geom_density2df(data=data, mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating', fill='..level..'), bins=20, show_legend=True) + scale_fill_brewer('div', palette='Spectral', direction=-1)  + coord_cartesian()

ggplot() + geom_density2df(data=data, mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating', fill='..level..'), bw=[0.25, 2.5], bins=20, show_legend=True) + scale_fill_brewer('div', palette='Spectral', direction=-1)  + coord_cartesian()

ggplot() + geom_density2df(data=data, mapping=aes(x='IMDB_Rating', y='Rotten_Tomatoes_Rating', fill='..group..'), show_legend=False) + scale_fill_brewer('div', palette='Spectral', direction=-1)  + coord_cartesian()

Voronoi diagrams

from vega_datasets import data

airports = data.airports()
flights = data.flights_airport()

airports

	iata	name	city	state	country	latitude	longitude
0	00M	Thigpen	Bay Springs	MS	USA	31.953765	-89.234505
1	00R	Livingston Municipal	Livingston	TX	USA	30.685861	-95.017928
2	00V	Meadow Lake	Colorado Springs	CO	USA	38.945749	-104.569893
3	01G	Perry-Warsaw	Perry	NY	USA	42.741347	-78.052081
4	01J	Hilliard Airpark	Hilliard	FL	USA	30.688012	-81.905944
...	...	...	...	...	...	...	...
3371	ZEF	Elkin Municipal	Elkin	NC	USA	36.280024	-80.786069
3372	ZER	Schuylkill Cty/Joe Zerbey	Pottsville	PA	USA	40.706449	-76.373147
3373	ZPH	Zephyrhills Municipal	Zephyrhills	FL	USA	28.228065	-82.155916
3374	ZUN	Black Rock	Zuni	NM	USA	35.083227	-108.791777
3375	ZZV	Zanesville Municipal	Zanesville	OH	USA	39.944458	-81.892105

3376 rows × 7 columns

ggplot() + geom_point(data=data, mapping=aes(x='longitude', y='latitude'))

counts = flights.merge(airports, left_on='origin', right_on='iata')
counts

	origin	destination	count	iata	name	city	state	country	latitude	longitude
0	ABE	ATL	853	ABE	Lehigh Valley International	Allentown	PA	USA	40.652363	-75.440402
1	ABE	BHM	1	ABE	Lehigh Valley International	Allentown	PA	USA	40.652363	-75.440402
2	ABE	CLE	805	ABE	Lehigh Valley International	Allentown	PA	USA	40.652363	-75.440402
3	ABE	CLT	465	ABE	Lehigh Valley International	Allentown	PA	USA	40.652363	-75.440402
4	ABE	CVG	247	ABE	Lehigh Valley International	Allentown	PA	USA	40.652363	-75.440402
...	...	...	...	...	...	...	...	...	...	...
5361	YUM	IPL	326	YUM	Yuma MCAS-Yuma International	Yuma	AZ	USA	32.656583	-114.605972
5362	YUM	LAS	99	YUM	Yuma MCAS-Yuma International	Yuma	AZ	USA	32.656583	-114.605972
5363	YUM	LAX	1044	YUM	Yuma MCAS-Yuma International	Yuma	AZ	USA	32.656583	-114.605972
5364	YUM	PHX	1961	YUM	Yuma MCAS-Yuma International	Yuma	AZ	USA	32.656583	-114.605972
5365	YUM	SLC	440	YUM	Yuma MCAS-Yuma International	Yuma	AZ	USA	32.656583	-114.605972

5366 rows × 10 columns

counts = counts[['origin', 'count', 'latitude', 'longitude']]
counts = counts.groupby('origin').agg({
    'count': 'sum',
    'latitude': 'first',
    'longitude': 'first',
})
counts

	count	latitude	longitude
origin
ABE	4807	40.652363	-75.440402
ABI	2660	32.411320	-99.681897
ABQ	41146	35.040222	-106.609194
ABY	1095	31.535515	-84.194473
ACK	457	41.253052	-70.060181
...	...	...	...
WYS	264	44.688399	-111.117638
XNA	14112	36.281869	-94.306811
YAK	725	59.503361	-139.660226
YKM	340	46.568170	-120.544059
YUM	3871	32.656583	-114.605972

303 rows × 3 columns

counts.query('(count > 2000) & (latitude > 25) & (latitude < 50)')

	count	latitude	longitude
origin
ABE	4807	40.652363	-75.440402
ABI	2660	32.411320	-99.681897
ABQ	41146	35.040222	-106.609194
ACV	3714	40.978115	-124.108619
AEX	2330	31.327372	-92.548556
...	...	...	...
TVC	4262	44.741445	-85.582235
TYS	14004	35.812487	-83.992856
VPS	6820	30.483250	-86.525400
XNA	14112	36.281869	-94.306811
YUM	3871	32.656583	-114.605972

194 rows × 3 columns

ggplot() + geom_point(data=counts.query('(count > 2000) & (latitude > 25) & (latitude < 50)'), mapping=aes(x='longitude', y='latitude'))

from scipy.spatial import Voronoi

v = Voronoi(points = counts[['longitude', 'latitude']].values)
v_df = pd.DataFrame([(i, *v.vertices[v_id]) for i, r in enumerate(v.regions) \
                                            for v_id in r if any(r) and not -1 in r],
                    columns=['id', 'x', 'y'])

ggplot() + \
    geom_polygon(aes(x='x', y='y', group='id', fill='id'), \
                 data=v_df, show_legend=False, color='black', alpha=0.) + \
    geom_point(aes(x='longitude', y='latitude'), data=counts, shape=21, color='black', fill='white') + \
    scale_fill_discrete() + scale_x_continuous(limits=[-125, -70]) + scale_y_continuous(limits=[25, 50]) + ggsize(1200, 800)

v.vertices[v.ridge_vertices].shape

(897, 2, 2)

v.vertices[0]

array([-122.52061032,  -13.33427622])

from scipy.spatial import Voronoi

v = Voronoi(points = counts[['longitude', 'latitude']].values)
v_df = pd.DataFrame([(v.vertices[r[0]].tolist() + v.vertices[r[1]].tolist()) for i, r in enumerate(v.ridge_vertices) if any(r) and not -1 in r],
                    columns=['x1', 'y1', 'x2', 'y2'])

ggplot() + \
    geom_segment(aes(x='x1', y='y1', xend='x2', yend='y2',), \
                 data=v_df, show_legend=False, color='black') + \
    geom_point(aes(x='longitude', y='latitude'), data=counts, shape=21, color='black', fill='white') + \
    scale_fill_discrete() + scale_x_continuous(limits=[-125, -70]) + scale_y_continuous(limits=[25, 50]) + ggsize(1200, 800)

v_df

	x1	y1	x2	y2
0	-162.443216	63.804203	-157.848949	64.376560
1	-162.443216	63.804203	-169.585957	56.901014
2	-157.848949	64.376560	-165.377812	49.900827
3	-169.585957	56.901014	-165.377812	49.900827
4	-156.615711	65.020800	-157.848949	64.376560
...	...	...	...	...
883	-90.176172	43.823228	-89.330875	43.993172
884	-88.647938	42.812487	-88.565038	43.432585
885	-91.127222	45.568721	-88.856878	45.038552
886	-88.306081	45.632959	-88.755175	45.108732
887	-87.172633	43.843489	-87.947783	43.726042

888 rows × 4 columns

ggplot() + geom_point(data=counts.query('(count > 2000) & (latitude > 25) & (latitude < 50)'), mapping=aes(x='longitude', y='latitude')) + geom_segment()